@inproceedings { sequeira11,
abstract = {Recently, a novel framework has been proposed for intrinsically motivated reinforcement learning (IMRL) in which a learning agent is driven by rewards that include not only information about what the agent must accomplish in order to “survive”, but also additional reward signals that drive the agent to engage in other activities, such as playing or exploring, because they are “inherently enjoyable”. In this paper, we investigate the impact of intrinsic motivation mechanisms in multiagent learning scenarios, by considering how such motivational system may drive an agent to engage in behaviors that are “socially aware”. We show that, using this approach, it is possible for agents to learn individually to acquire socially aware behaviors that tradeoff individual well-fare for social acknowledgment, leading to a more successful performance of the population as a whole.},
booktitle = {Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics},
keywords = {Reinforcement Learning;Multi-Agent Societies;},
pages = {1--6},
publisher = {IEEE},
series = {ICDL-EpiRob 2011},
title = {Emerging social awareness: Exploring intrinsic motivation in multiagent learning},
volume = {2},
year = {2011},
author = {Pedro Sequeira and Francisco S. Melo and Rui Prada and Ana Paiva}
}